def plot_of_unit_loads(c: Config):
"""Make a contour plot of response at unit load position."""
fem_runner = OSRunner(c)
response_type = ResponseType.YTranslation
X, Z, R = [], [], []
for x in np.linspace(c.bridge.x_min, c.bridge.x_max, int(c.bridge.length)):
X.append([])
Z.append([])
R.append([])
for z in np.linspace(c.bridge.z_min, c.bridge.z_max,
int(c.bridge.width)):
pload = PointLoad(x_frac=c.bridge.x_frac(x),
z_frac=c.bridge.z_frac(z),
kn=100)
fem_params = SimParams(ploads=[pload],
response_types=[response_type])
fem_responses = load_fem_responses(
c=c,
fem_params=fem_params,
response_type=response_type,
fem_runner=fem_runner,
)
X[-1].append(x)
Z[-1].append(z)
R[-1].append(fem_responses._at(x=x, y=0, z=z))
cmap = get_cmap("bwr")
plt.contourf(X, Z, R, levels=50, cmap=cmap)
plt.show()
def run_uls(
c: Config,
piers: bool,
healthy: bool,
cracked: bool,
crack_x: Optional[int] = None,
crack_length: Optional[int] = None,
):
"""Run all unit load simulations."""
def crack_f():
return transverse_crack(at_x=crack_x, length=crack_length)
print_i(
f"Running simulations with crack zone at x = {crack_x}, length = {crack_length}"
)
response_type = ResponseType.YTranslation
if piers:
# Pier settlement.
list(
PSResponses.load(c=c,
response_type=response_type,
fem_runner=OSRunner(c)))
if healthy:
c = healthy_damage_w_crack_nodes(crack_f=crack_f).use(c)[0]
# Unit load simulations (healthy bridge).
ULResponses.load_wheel_tracks(
c=c,
response_type=response_type,
sim_runner=OSRunner(c),
wheel_zs=c.bridge.wheel_track_zs(c),
run_only=True,
)
elif cracked:
# Unit load simulations (cracked bridge).
c = crack_f().use(c)[0]
ULResponses.load_wheel_tracks(
c=c,
response_type=response_type,
sim_runner=OSRunner(c),
wheel_zs=c.bridge.wheel_track_zs(c),
run_only=True,
)
def run_ulm(c: Config, healthy: bool, cracked: bool, x_i: float, z_i: float):
"""Run all unit load simulations."""
response_type = ResponseType.YTranslation
wheel_xs = c.bridge.wheel_track_xs(c)
wheel_x = wheel_xs[x_i]
wheel_zs = c.bridge.wheel_track_zs(c)
wheel_z = wheel_zs[z_i]
print_i(f"Wheel (x, z) = ({wheel_x}, {wheel_z})")
point = Point(x=wheel_x, y=0, z=wheel_z)
if healthy:
ULResponses.load_ulm(
c=c,
response_type=response_type,
points=[point],
sim_runner=OSRunner(c),
)
if cracked:
c = transverse_crack().use(c)[0]
ULResponses.load_ulm(
c=c,
response_type=response_type,
points=[point],
sim_runner=OSRunner(c),
)
def cover_photo(c: Config, x: float, deformation_amp: float):
"""
TODO: SimParams takes any loads iterable, to be flattened.
TODO: Wrap SimRunner into Config.
TODO: Ignore response type in SimParams (fill in by load_sim_responses).
"""
response_type = ResponseType.YTranslation
sim_responses = load_fem_responses(
c=c,
sim_runner=OSRunner(c),
response_type=response_type,
sim_params=SimParams(
response_types=[response_type],
ploads=list(
chain.from_iterable(
truck1.to_point_loads(
bridge=c.bridge,
time=truck1.time_at(x=x, bridge=c.bridge),
))),
),
)
shells = contour_responses_3d(c=c, sim_responses=sim_responses)
for cmap in [
parula_cmap,
get_cmap("jet"),
get_cmap("coolwarm"),
get_cmap("viridis"),
]:
contour_responses_3d(
c=c,
sim_responses=sim_responses,
deformation_amp=deformation_amp,
shells=shells,
cmap=cmap,
)
plt.axis("off")
plt.grid(False)
plt.savefig(
c.get_image_path(
"cover-photo",
f"cover-photo-deform-{deformation_amp}"
f"-cmap-{cmap.name}.pdf",
))
plt.close()
def oneclass(c: Config):
normal_traffic_array, traffic_scenario = load_normal_traffic_array(c)
bridge_scenarios = [HealthyScenario()] + each_pier_scenarios(c)
response_type = ResponseType.YTranslation
points = [
Point(x=x, y=0, z=z)
for x, z in itertools.product(
np.linspace(c.bridge.x_min, c.bridge.x_max / 2, 20),
np.linspace(c.bridge.z_min, c.bridge.z_max / 2, 3),
)
]
results = []
for b, bridge_scenario in enumerate(bridge_scenarios):
print_i(f"One class: bridge scenario {bridge_scenario.name}")
responses = responses_to_traffic_array(
c=c,
traffic_array=normal_traffic_array,
response_type=response_type,
bridge_scenario=bridge_scenario,
points=points,
fem_runner=OSRunner(c),
).T
print(len(normal_traffic_array))
print(responses.shape)
# Fit on the healthy scenario.
if b == 0:
assert len(responses) == len(points)
clfs = []
for r, rs in enumerate(responses):
print_i(f"Training classifier {r} / {len(responses)}")
clfs.append(OneClassSVM().fit(rs.reshape(-1, 1)))
scenario_results = []
for p, _ in enumerate(points):
print_i(f"Predicting points {p} / {len(points)}")
prediction = clfs[p].predict(responses[p].reshape(-1, 1))
print(prediction)
print(len(prediction[prediction < 0]))
print(len(prediction[prediction > 0]))
def gradient_pier_displacement_plot(
c: Config,
pier_disp: PierSettlementScenario,
response_type: ResponseType,
title: str,
):
"""Contour plot of piers displaced in an increasing gradient."""
# 10 x 10 grid of points on the bridge deck where to record fem.
points = [
Point(x=x, y=0, z=z) for x, z in itertools.product(
np.linspace(c.bridge.x_min, c.bridge.x_max, 10),
np.linspace(c.bridge.z_min, c.bridge.z_max, 10),
)
]
# Create empty traffic array and collect fem.
response_array = responses_to_traffic_array(
c=c,
traffic_array=np.zeros(
(1, len(c.bridge.wheel_tracks(c)) * c.il_num_loads)),
response_type=response_type,
bridge_scenario=pier_disp,
points=points,
fem_runner=OSRunner(c),
)
top_view_bridge(c.bridge, abutments=True, piers=True)
responses = Responses.from_responses(
response_type=response_type,
responses=[(response_array[0][p], point)
for p, point in enumerate(points)],
)
plot_contour_deck(c=c, responses=responses, center_norm=True)
plt.title(title)
plt.savefig(
c.get_image_path("pier-scenarios",
f"pier-displacement-{safe_str(title)}"))
plt.close()
def number_of_uls_plot(c: Config):
"""Plot error as a function of number of unit load simulations."""
if not c.shorten_paths:
raise ValueError("This plot requires --shorten-paths true")
response_type = ResponseType.YTranslation
num_ulss = np.arange(100, 2000, 10)
chosen_uls = 600
point = Point(x=c.bridge.x_max - (c.bridge.length / 2), y=0, z=-8.4)
wagen1_time = truck1.time_at(x=point.x, bridge=c.bridge)
print_i(f"Wagen 1 time at x = {point.x:.3f} is t = {wagen1_time:.3f}")
# Determine the reference value.
truck_loads = flatten(
truck1.to_point_load_pw(time=wagen1_time, bridge=c.bridge), PointLoad)
print_i(f"Truck loads = {truck_loads}")
sim_responses = load_fem_responses(
c=c,
response_type=response_type,
sim_runner=OSRunner(c),
sim_params=SimParams(ploads=truck_loads,
response_types=[response_type]),
)
ref_value = sim_responses.at_deck(point, interp=True) * 1000
print_i(f"Reference value = {ref_value}")
# Collect the data.
total_load = []
num_loads = []
responses = []
for num_uls in num_ulss:
c.il_num_loads = num_uls
# Nested in here because it depends on the setting of 'il_num_loads'.
truck_loads = flatten(
truck1.to_wheel_track_loads(c=c, time=wagen1_time), PointLoad)
num_loads.append(len(truck_loads))
total_load.append(sum(map(lambda l: l.kn, truck_loads)))
sim_responses = load_fem_responses(
c=c,
response_type=response_type,
sim_runner=OSRunner(c),
sim_params=SimParams(ploads=truck_loads,
response_types=[response_type]),
)
responses.append(sim_responses.at_deck(point, interp=True) * 1000)
# Plot the raw fem, then error on the second axis.
plt.landscape()
# plt.plot(num_ulss, fem)
# plt.ylabel(f"{response_type.name().lower()} (mm)")
plt.xlabel("ULS")
error = np.abs(np.array(responses) - ref_value).flatten() * 100
# ax2 = plt.twinx()
plt.plot(num_ulss, error)
plt.ylabel("Error (%)")
plt.title(
f"Error in {response_type.name()} to Truck 1 as a function of ULS")
# Plot the chosen number of ULS.
chosen_error = np.interp([chosen_uls], num_ulss, error)[0]
plt.axhline(
chosen_error,
label=f"At {chosen_uls} ULS, error = {np.around(chosen_error, 2)} %",
color="black",
)
plt.axhline(0,
color="red",
label="Response from direct simulation (no wheel tracks)")
plt.legend()
plt.tight_layout()
plt.savefig(c.get_image_path("paramselection", "uls.pdf"))
plt.close()
# Additional verification plots.
plt.plot(num_ulss, total_load)
plt.savefig(c.get_image_path("paramselection",
"uls-verify-total-load.pdf"))
plt.close()
plt.plot(num_ulss, num_loads)
plt.savefig(c.get_image_path("paramselection", "uls-verify-num-loads.pdf"))
plt.close()
def events(c: Config, x: float, z: float):
"""Plot events due to normal traffic."""
point = Point(x=x, y=0, z=z)
# 10 seconds of 'normal' traffic.
max_time = 10
traffic_scenario = normal_traffic(c=c, lam=5, min_d=2)
# Create the 'TrafficSequence' and 'TrafficArray'.
traffic_sequence = traffic_scenario.traffic_sequence(
bridge=c.bridge, max_time=max_time
)
traffic_array = to_traffic_array(
c=c, traffic_sequence=traffic_sequence, max_time=max_time
)
# Find when the simulation has warmed up, and when 'TrafficArray' begins.
warmed_up_at = traffic_sequence[0][0].time_left_bridge(c.bridge)
traffic_array_starts = (int(warmed_up_at / c.sensor_hz) + 1) * c.sensor_hz
print(f"warmed up at = {warmed_up_at}")
print(f"traffic_array_starts = {traffic_array_starts}")
traffic_array_ends = traffic_array_starts + (len(traffic_array) * c.sensor_hz)
print(f"traffic_array_ends = {traffic_array_ends}")
point_lane_ind = c.bridge.closest_lane(z)
vehicles = list(set(ts[0] for ts in traffic_sequence))
print(len(vehicles))
print(vehicles[0])
vehicles = sorted(
set(ts[0] for ts in traffic_sequence if ts[0].lane == point_lane_ind),
key=lambda v: -v.init_x_frac,
)
print(len(vehicles))
print(vehicles[0])
event_indices = []
vehicle_times = [v.time_at(x=x - 2, bridge=c.bridge) for v in vehicles]
for v, t in zip(vehicles, vehicle_times):
print(f"Vehicle {v.init_x_frac} {v.mps} at time {t}")
start_time = int(t / c.sensor_hz) * c.sensor_hz
print(f"start_time = {start_time}")
ta_start_time = np.around(start_time - traffic_array_starts, 8)
print(f"ta start time = {ta_start_time}")
ta_start_index = int(ta_start_time / c.sensor_hz)
print(f"ta start index = {ta_start_index}")
ta_end_index = ta_start_index + int(c.event_time_s / c.sensor_hz)
print(f"ta end index = {ta_end_index}")
if ta_start_index >= 0 and ta_end_index < len(traffic_array):
event_indices.append((ta_start_index, ta_end_index))
print(event_indices)
responses = (
responses_to_traffic_array(
c=c,
traffic_array=traffic_array,
response_type=ResponseType.YTranslation,
damage_scenario=healthy_scenario,
points=[point],
sim_runner=OSRunner(c),
)
* 1000
)
# fem = add_displa_noise(fem)
print(responses.shape)
plt.portrait()
for event_ind, (event_start, event_end) in enumerate(event_indices):
plt.subplot(len(event_indices), 1, event_ind + 1)
plt.plot(responses[event_start : event_end + 1])
plt.tight_layout()
plt.savefig(c.get_image_path("classify/events", "events.pdf"))
plt.close()
def comparison_plots_705(c: Config, run_only: bool, scatter: bool):
"""Make contour plots for all verification points on bridge 705."""
# from classify.scenario.bridge import transverse_crack
# c = transverse_crack().use(c)[0]
positions = [
# (52, -8.4, "a"),
(34.95459, 26.24579 - 16.6, "a"),
(51.25051, 16.6 - 16.6, "b"),
(89.98269, 9.445789 - 16.6, "c"),
(102.5037, 6.954211 - 16.6, "d"),
# (34.95459, 29.22606 - 16.6, "a"),
# (51.25051, 16.6 - 16.6, "b"),
# (92.40638, 12.405 - 16.6, "c"),
# (101.7649, 3.973938 - 16.6, "d"),
]
diana_values = pd.read_csv("validation/diana-screenshots/min-max.csv")
response_types = [ResponseType.YTranslation, ResponseType.Strain]
# For each response type and loading position first create contour plots for
# OpenSees. Then finally create subplots comparing to Diana.
cmap = diana_cmap_r
for load_x, load_z, label in positions:
for response_type in response_types:
# Setup the metadata.
if response_type == ResponseType.YTranslation:
rt_str = "displa"
unit_str = "mm"
elif response_type == ResponseType.Strain:
rt_str = "strain"
unit_str = "E-6"
else:
raise ValueError("Unsupported response type")
row = diana_values[diana_values["name"] == f"{label}-{rt_str}"]
dmin, dmax = float(row["dmin"]), float(row["dmax"])
omin, omax = float(row["omin"]), float(row["omax"])
amin, amax = max(dmin, omin), min(dmax, omax)
levels = np.linspace(amin, amax, 16)
# Create the OpenSees plot.
loads = [
PointLoad(
x_frac=c.bridge.x_frac(load_x),
z_frac=c.bridge.z_frac(load_z),
kn=100,
)
]
fem_responses = load_fem_responses(
c=c,
response_type=response_type,
sim_runner=OSRunner(c),
sim_params=SimParams(ploads=loads,
response_types=response_types),
)
if run_only:
continue
title = (
f"{response_type.name()} from a {loads[0].kn} kN point load at"
+ f"\nx = {load_x:.3f}m, z = {load_z:.3f}m, with ")
save = lambda prefix: c.get_image_path(
"validation/diana-comp",
safe_str(f"{prefix}{response_type.name()}") + ".pdf",
)
top_view_bridge(c.bridge, piers=True, abutments=True)
fem_responses = fem_responses.resize()
sci_format = response_type == ResponseType.Strain
plot_contour_deck(
c=c,
responses=fem_responses,
ploads=loads,
cmap=cmap,
levels=levels,
sci_format=sci_format,
decimals=4,
scatter=scatter,
)
plt.title(title + "OpenSees")
plt.tight_layout()
plt.savefig(save(f"{label}-"))
plt.close()
# Finally create label/title the Diana plot.
if label is not None:
# First plot and clear, just to have the same colorbar.
plot_contour_deck(c=c,
responses=fem_responses,
ploads=loads,
cmap=cmap,
levels=levels)
plt.cla()
# Then plot the bridge and
top_view_bridge(c.bridge, piers=True, abutments=True)
plt.imshow(
mpimg.imread(
f"validation/diana-screenshots/{label}-{rt_str}.png"),
extent=(
c.bridge.x_min,
c.bridge.x_max,
c.bridge.z_min,
c.bridge.z_max,
),
)
dmin_s = f"{dmin:.4e}" if sci_format else f"{dmin:.4f}"
dmax_s = f"{dmax:.4e}" if sci_format else f"{dmax:.4f}"
dabs_s = (f"{abs(dmin - dmax):.4e}"
if sci_format else f"{abs(dmin - dmax):.4f}")
for point, leg_label, color, alpha in [
((load_x, load_z), f"{loads[0].kn} kN load", "r", 1),
((0, 0), f"min = {dmin_s} {fem_responses.units}", "r", 0),
((0, 0), f"max = {dmax_s} {fem_responses.units}", "r", 0),
((0, 0), f"|min-max| = {dabs_s} {fem_responses.units}",
"r", 0),
]:
plt.scatter(
[point[0]],
[point[1]],
label=leg_label,
marker="o",
color=color,
alpha=alpha,
)
plt.legend()
plt.title(title + "Diana")
plt.xlabel("X position (m)")
plt.ylabel("Z position (m)")
plt.tight_layout()
plt.savefig(save(f"{label}-diana-"))
plt.close()
def piers_displaced(c: Config):
"""Contour plots of pier displacement for the given pier indices."""
pier_indices = [4, 5]
response_types = [ResponseType.YTranslation, ResponseType.Strain]
axis_values = pd.read_csv("validation/axis-screenshots/piers-min-max.csv")
for r_i, response_type in enumerate(response_types):
for p in pier_indices:
# Run the simulation and collect fem.
sim_responses = load_fem_responses(
c=c,
response_type=response_type,
sim_runner=OSRunner(c),
sim_params=SimParams(displacement_ctrl=PierSettlement(
displacement=c.pd_unit_disp, pier=p), ),
)
# In the case of stress we map from kn/m2 to kn/mm2 (E-6) and then
# divide by 1000, so (E-9).
assert c.pd_unit_disp == 1
if response_type == ResponseType.Strain:
sim_responses.to_stress(c.bridge).map(lambda r: r * 1e-9)
# Get min and max values for both Axis and OpenSees.
rt_str = ("displa" if response_type == ResponseType.YTranslation
else "stress")
row = axis_values[axis_values["name"] == f"{p}-{rt_str}"]
dmin, dmax = float(row["dmin"]), float(row["dmax"])
omin, omax = float(row["omin"]), float(row["omax"])
amin, amax = max(dmin, omin), min(dmax, omax)
levels = np.linspace(amin, amax, 16)
# Plot and save the image. If plotting strains use Axis values for
# colour normalization.
# norm = None
from plot import axis_cmap_r
cmap = axis_cmap_r
top_view_bridge(c.bridge, abutments=True, piers=True)
plot_contour_deck(c=c,
cmap=cmap,
responses=sim_responses,
levels=levels)
plt.tight_layout()
plt.title(
f"{sim_responses.response_type.name()} from 1mm pier settlement with OpenSees"
)
plt.savefig(
c.get_image_path(
"validation/pier-displacement",
safe_str(f"pier-{p}-{sim_responses.response_type.name()}")
+ ".pdf",
))
plt.close()
# First plot and clear, just to have the same colorbar.
plot_contour_deck(c=c,
responses=sim_responses,
cmap=cmap,
levels=levels)
plt.cla()
# Save the axis plots.
axis_img = mpimg.imread(
f"validation/axis-screenshots/{p}-{rt_str}.png")
top_view_bridge(c.bridge, abutments=True)
plt.imshow(
axis_img,
extent=(
c.bridge.x_min,
c.bridge.x_max,
c.bridge.z_min,
c.bridge.z_max,
),
)
# Plot the load and min, max values.
for point, leg_label, color in [
((0, 0), f"min = {np.around(dmin, 3)} {sim_responses.units}",
"r"),
((0, 0), f"max = {np.around(dmax, 3)} {sim_responses.units}",
"r"),
(
(0, 0),
f"|min-max| = {np.around(abs(dmax - dmin), 3)} {sim_responses.units}",
"r",
),
]:
plt.scatter(
[point[0]],
[point[1]],
label=leg_label,
marker="o",
color=color,
alpha=0,
)
if response_type == ResponseType.YTranslation:
plt.legend()
# Title and save.
plt.title(
f"{response_type.name()} from 1mm pier settlement with AxisVM")
plt.xlabel("X position (m)")
plt.ylabel("Z position (m)")
plt.tight_layout()
plt.savefig(
c.get_image_path(
"validation/pier-displacement",
f"{p}-axis-{rt_str}.pdf",
))
plt.close()
def point_load_response_plots(c: Config,
x: float,
z: float,
kn: int = 1000,
run: bool = False):
"""Response to a point load per scenarios scenario."""
response_types = [ResponseType.YTranslation, ResponseType.Strain]
# scenarios = all_scenarios(c)
damage_scenarios = [HealthyScenario(), transverse_crack()]
# 10 x 10 grid of points on the bridge deck where to record fem.
points = [
Point(x=x, y=0, z=z) for x, z in itertools.product(
np.linspace(c.bridge.x_min, c.bridge.x_max, 30),
np.linspace(c.bridge.z_min, c.bridge.z_max, 100),
)
]
for response_type in response_types:
all_responses = []
for damage_scenario in damage_scenarios:
sim_params = SimParams(
response_types=[response_type],
ploads=[
PointLoad(x_frac=c.bridge.x_frac(x),
z_frac=c.bridge.z_frac(z),
kn=kn)
],
)
use_c, sim_params = damage_scenario.use(c=c, sim_params=sim_params)
all_responses.append(
load_fem_responses(
c=use_c,
sim_params=sim_params,
response_type=response_type,
sim_runner=OSRunner(use_c),
run=run,
).resize())
amin, amax = np.inf, -np.inf
for sim_responses in all_responses:
responses = np.array(list(sim_responses.values()))
amin = min(amin, min(responses))
amax = max(amax, max(responses))
for d, damage_scenario in enumerate(damage_scenarios):
top_view_bridge(c.bridge, abutments=True, piers=True)
plot_contour_deck(
c=c,
responses=all_responses[d],
levels=100,
norm=colors.Normalize(vmin=amin, vmax=amax),
decimals=10,
)
plt.title(damage_scenario.name)
plt.tight_layout()
plt.savefig(
c.get_image_path(
"contour/point-load",
safe_str(
f"x-{x:.2f}-z-{z:.2f}-kn-{kn}-{response_type.name()}-{damage_scenario.name}"
) + ".pdf",
))
plt.close()